数字农科院2.0

Estimation of Winter Wheat Biomass Using Visible Spectral and BP Based Artificial Neural Networks

文献类型： 外文期刊

作者： Cui Ri-xian;Liu Ya-dong;Fu Jin-dong

作者机构：

关键词： Winter wheat;Visible spectrum;Color indices;Canopy cover;Above ground Biomass;BP based artificial neural networks

期刊名称： SPECTROSCOPY AND SPECTRAL ANALYSIS

ISSN： 1000-0593

年卷期： 2015 年 35 卷 9 期

页码：

收录情况： SCI

摘要： The objective of this study was to evaluate the feasibility of using color digital image analysis and back propagation (BP) based artificial neural networks (ANN) method to estimate above ground biomass at the canopy level of winter wheat field. Digital color images of winter wheat canopies grown under six levels of nitrogen treatments were taken with a digital camera for four times during the elongation stage and at the same time wheat plants were sampled to measure above ground biomass. Canopy cover (CC) and 10 color indices were calculated from winter wheat canopy images by using image analysis program (developed in Microsoft Visual Basic). Correlation analysis was carried out to identify the relationship between CC, 10 color indices and winter wheat above ground biomass. Stepwise multiple linear regression and BP based ANN methods were used to establish the models to estimate winter wheat above ground biomass. The results showed that CC, and two color indices had a significant correlation with above ground biomass. CC revealed the highest correlation with winter wheat above ground biomass. Stepwise multiple linear regression model constituting CC and color indices of NDI and b, and BP based ANN model with four variables (CC, g, b and NDI) for input was constructed to estimate winter wheat above ground biomass. The validation results indicate that the model using BP based ANN method has a better performance with higher R-2 (0.903) and lower RMSE (61.706) and RRMSE (18.876) in comparation with the stepwise regression model.

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